AI MVP development with the price on the page, whichever kind of AI MVP you mean
Search for AI MVP development and you get two different things. One is an MVP built fast with AI coding tools. The other is an MVP whose product is AI: an assistant that answers from your documents, or a tool that drafts or sorts. We do both. Senior engineers build with AI tools and read every line, and the prices are the ones we publish: a $3,000 fixed sprint for a prototype you built in Lovable, Replit or Cursor, and $15,000 to $30,000 for a typical 1 to 2 month build, plus the monthly model bill worked out at October 2026 list prices.
What people mean by AI MVP development, and which one you need
Ask ten founders what an AI MVP is and you get two answers. Some mean any MVP built faster with AI coding tools. Others mean a product with AI inside it. Most people who write to us are a third kind: they built something with an AI tool themselves and now need an engineer who'll keep it rather than start over. The price depends on which you are.
An MVP built with AI tools
The build method. Engineers use Cursor, Claude Code and similar tools to write and test code faster, and the output is ordinary software, a web app or a mobile app. The questions for you are who reads the code and who gets the saving.
An MVP whose product is AI
The product. An assistant that answers from your documents, or a tool that drafts, sorts or searches by meaning, built on a hosted model plus retrieval over your data, with an evaluation set and guardrails. The questions are what the AI part adds to the price, and what the model costs each month.
A prototype you built yourself
The most common arrival. Lovable, Replit, Bolt or Cursor got you a working demo, maybe users. Now it needs sign-in, permissions, payments and a deploy that holds, and you want it kept and repaired rather than thrown away.
An MVP built with AI tools, by engineers who read the code
Every developer you could hire in 2026 uses AI tools. The difference is whether the person behind them can explain the result. Our engineers write the first draft of code and tests with the tools, then do the work the tools skip: design the data model, review every change, write the tests, run the security checks, and read every line before it ships. The saving is yours because the prices are fixed or flat. When the tools make us faster, you pay for fewer months.
What the tools leave for last
Builders tell us the app is 95% done. The missing 5% is usually the part that was never built, and it's the same list every time:
- Sign-in checked on the server, so a visitor cannot skip the login screen and call the data directly
- Who can see what: each customer's records kept separate at the database level
- Payments, refunds and subscription changes that match what the invoice says
- A deploy with backups, a rollback and secrets kept out of the code
- Tests that run on every change, so a fix on Tuesday does not break Monday's feature
- Error monitoring, so you hear about a crash before a customer does
What a paid audit of one Lovable app found
The owner of a booking app built with Lovable paid for an audit and posted the findings (r/lovable, June 2026): "Admin authentication is entirely client-side with password exposed in client bundle ... All booking and admin API endpoints are completely unauthenticated". The app worked. Every screen rendered. Anyone who knew the address could also read every booking. That's the gap between a demo and a product, and it's what the $3,000 sprint closes first.
One founder described the arrival at that gap (r/SaaS, February 2026): "The thing that took three weeks to build took five months to make production-ready." The sprint is one week, and the roadmap tells you what the months after it hold.
After the sprint, two paths. You keep building with your own AI tools and we review what you ship, which is the Senior Product Engineer retainer at $4,000 a month half-day. Or we finish the product on the retainer with the CTO and the team. Either way the roadmap is yours, and nothing in the price punishes you for taking it to another builder. If the prototype was built for you by a developer who then left, our software project rescue page covers the takeover.
An MVP whose product is AI, with the model bill worked out
An AI product MVP has four parts. The model is a hosted API from OpenAI, Anthropic or another provider. Retrieval over your data means your documents are split, indexed and searched so the model answers from your material. The product around it is accounts, billing, admin and logs. The evaluation set is a list of real questions with known good answers, run after every change. The AI part is usually the smaller share of the work. Continuum says the same of its builds (September 2026): "Accounts, billing, onboarding, admin, and monitoring are the larger share, and they are there whether or not the product uses AI."
What the build costs
An API-wrapper or retrieval MVP is a startup MVP plus one component, so it sits in the same window as any first version we build: 1 to 2 months on the $15,000 a month retainer, so $15,000 to $30,000, CTO and engineers included. Data preparation (messy documents, several sources, permissions to respect) and compliance push it toward the top of that window; SFAI Labs puts HIPAA, SOC 2 or GDPR requirements at 30% to 100% on top (February 2026). A custom or fine-tuned model is a different project. Vendors price those at $100,000 and up, and the scope will say so before you commit. If the records the assistant must read live in a system that's itself due for replacement, the retrieval layer and the replacement are planned together; our legacy software modernization page covers that side.
Evaluation, observability and guardrails are scoped in from the start. Bitsens budgets them at 15% to 30% of the AI portion of a build (May 2026). Skipping them is how a demo that impressed the team becomes a product that answers wrong in front of a customer.
What the model costs each month
Take 1,000 active users making 30 requests a month, each about 2,000 input and 500 output tokens. That's 60 million input and 15 million output tokens a month. At list prices on October 17, 2026 (OpenAI, Anthropic; per million tokens, in and out):
- About $13.50 a month on Claude Haiku 5.5 or gpt-6-luna ($0.10 in, $0.50 out), the tier for sorting, extraction and routing.
- $270 a month on Claude Sonnet 5.5 or gpt-6.1-sol ($2 in, $10 out), where most assistants over company data sit.
- $540 a month on Claude Opus 5.5 ($4 in, $20 out).
- $1,350 a month on Claude Fable 5.1 or gpt-6-astra ($10 in, $50 out), the top tier.
Prompt caching and batch tiers lower these; a long document in every prompt raises them. For comparison, Bitsens' guide puts the running bill at "$200 to $2,000 a month at the low end, $5,000 to $25,000 a month once you have real users and a real LLM bill" (May 2026). The scope names the model and the estimate; the monthly scorecard reports what it came to.
AI MVP development pricing, one table for every case
Find your row. Every price is one of the three rates we publish, and the arithmetic is shown. How the $15,000 retainer compares with advise-only CTO rates is in our fractional CTO cost guide.
| What you have | First step | Price | Then |
|---|---|---|---|
| A prototype built with Lovable, Replit, Bolt or Cursor | Rescue Sprint: audit, critical fixes, scorecard and roadmap | $3,000 fixed, one week | Keep building yourself with our review (one senior engineer, $4,000 a month half-day), or we finish it on the retainer. |
| A brief for a product built with AI tools, no AI feature | Scope-and-estimate call, then the Technical Leadership retainer | $15,000 a month; a typical MVP ships in 1 to 2 months, so $15,000 to $30,000 | Step back to $0 a month, keep one engineer, or keep the team. |
| A brief for an AI product: assistant, retrieval over your data, drafting or sorting | Scope-and-estimate call with a data inventory and an evaluation set, then the same retainer | Same $15,000 a month and the same window for an API-wrapper or retrieval MVP, plus the model bill: $13.50 to $1,350 a month in the worked example above | Same choice, with model spend reported in the monthly scorecard. |
| A technical lead who needs hands | Senior Product Engineer | $4,000 a month half-day, or $8,000 a month full-time | Month-to-month, under your lead's decisions. |
Where the $3,000 sits against what people report: a Lovable user was quoted $8,000 to redevelop the whole app and called it nuts (October 2025); a non-developer paid $4,200 all in to have an AI-built app finished (July 2025); a small shop priced the rebuild of a vibe-coded v1 with paying users at $27,000, with untangling a third of the budget (June 2026); and Fora Soft lists an audit of a single-service MVP at $2,000 to $8,000 for its own team against a typical market band of $15,000 to $40,000 (a vendor's figure, October 2026). The sprint includes the fixing, which an audit alone does not. For a build from a brief, the sourced market ranges are on our MVP development page and in the MVP development cost guide.
Guarantees on every plan
- Month-to-month, 30 days' notice, no termination fees.
- First 30 days: full money-back guarantee.
- IP yours from the first commit under a US-jurisdiction agreement; repos and servers in your own accounts.
- Any engineer replaced within 7 days if not a fit.
- Happiness Index: you score us every month on delivery, accepted work and incidents. A month under 50 is refunded.
How an AI MVP build runs, and what you hold after each step
The same five steps for both meanings. The sprint replaces the scope step when you arrive with a prototype.
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Discovery call
Thirty minutes with Tim, free. You leave with an honest read on which AI MVP you need, and whether your prototype is a keep-and-repair case or a rebuild.
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Sprint, or scope
With a prototype, the $3,000 one-week sprint. With a new product, a written scope, architecture, timeline and estimate; for an AI product that includes the data inventory, the evaluation set and the chosen model with its monthly estimate.
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Design
Screens and the data model drawn together. For an AI product, also the prompts, the retrieval design and what the product does when the model is unsure. What you approve is what gets built.
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Build in two-week cycles
Senior engineers build with AI tools and read every line. After each cycle there's a working version, the evaluation results, code in your repos and AI accounts in your organisation.
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Launch and after
Staged rollout, monitoring of errors and model spend, documentation and a handover session. The product is live and yours. Go to $0 a month, keep one engineer, or keep the team.
Working beside a founder who keeps building with AI tools has one rule we write down on day one: who owns which part of the codebase, and which changes get reviewed before they go live. Without that the handoffs blur, and the founder ends up re-fixing what the tool undid. With it, you keep your speed and we keep the foundations sane.
An AI MVP development company that built its own AI product first
In 2023 and 2024, before AI coding agents existed and "AI development" meant pasting one file at a time into ChatGPT, we built Gemina AI: one of the first AI coding tools that understood a whole codebase on disk, applied edits across many files inside VS Code, and used retrieval-augmented generation to manage context. We retired it once modern agent tools caught up, and we've used those tools to build for clients since. What we've built for clients, with screenshots, is on the projects page.
The people who do the work
I'm Tim Tran, Deepsel's founder. I take the discovery call, I run the sprint audit, and I lead every build. Khuong Pham (7+ years, full-stack React and Python) and Trang Hoang (QA, 6 years, test automation) do the building and the testing. The team works from Hanoi, Vietnam, on a daily overlap window with US hours, under Deepsel Inc., a Delaware C corp. We keep it small on purpose: a handful of retainer clients at a time, and a waitlist when we're full.
What you can check
- Clubloungery and FitFoodies are live. Open them and judge the work.
- The sprint ends in a written scorecard you keep, usable with us or with anyone else.
- Ask for a past client who was non-technical, and ask them what broke after launch and how it was handled.
AI MVP development vs the tools alone, an app-builder vendor, an AI agency or a hire
What each route costs in 2026, with sources, and the point at which it stops being the right one.
Keep going alone with the tools
Lovable's Pro plans run $25 a month for 100 credits up to $2,250 for 10,000 (Lovable, October 2026); Bolt Pro is $25; Replit Core is $20 and Pro $100 billed annually; Cursor's listed tiers run $8 to $200 (live pages, October 2026). Reported spend runs from about $500 for a product to $4,000 for a CRM, and heavy users describe $1,500 to $6,000 a month. Right while you're testing the idea. Wrong once strangers' data is in it.
An AI app-builder vendor
One builder's own comparison table lists "Development labor $0" for its tool against $15,000 to $50,000 for a freelancer and $50,000 to $150,000 for an agency (NxCode, a vendor's figure, October 2026). Zero labor holds until the first customer asks who can see their data. Then you need an engineer.
An AI-specialist agency
Published vendor ranges for an MVP whose product is AI: an API wrapper at $5,000 to $15,000 and retrieval over your own data at $25,000 to $60,000 (Space-O, October 2026); $15,000 to $40,000 in 3 to 6 weeks for a basic build and $40,000 to $120,000 in 8 to 14 weeks for a custom retrieval system (SFAI Labs, February 2026); $35,000 to $80,000 for an 8 to 14 week build at an AI-native agency (Bitsens, May 2026). All vendors' figures. A GoodFirms survey (September 2026) puts AI-powered small projects at $50,000 to $125,000. Our guide on how to choose a software development company lists the questions to ask any of them.
Hire an AI engineer
New engineering hires on Carta averaged about $189,000 in June 2025, the highest of any job function. Benefits add about 30% of employer cost (BLS, June 2026), a recruiter takes 15% to 30% of first-year salary (Dover, August 2026), and a senior opening takes about 71 days to fill (Ashby via Ideaware, September 2026). The right call once the product has users and a model bill worth an engineer's full attention.
Ours is $3,000 to make what you built safe to charge for, or $15,000 to $30,000 for a typical build from a brief, AI feature or not, with the model bill estimated in writing and the IP yours from the first commit. If your first version is a multi-tenant product with billing and roles, read the SaaS development page and the SaaS development cost guide. The software development for startups page covers what comes after the first version, our list of the best small software development companies for startups names the competitors, and the fractional CTO page is for founders who need a named senior person on an investor call.
AI MVP development questions founders ask
What is an AI MVP?
Two things, depending on who is talking. An MVP built with AI tools is ordinary software written faster with Cursor, Claude Code or an app builder like Lovable. An MVP whose product is AI is a first version of an assistant, a document search or a drafting tool, built on a hosted model such as OpenAI's or Anthropic's with retrieval over your own data. This page prices both; the general MVP development page covers a first version without an AI feature.
How much does AI MVP development cost?
At Deepsel: $3,000 fixed for the one-week sprint on a prototype you built; $15,000 to $30,000 for a typical MVP, which is 1 to 2 months on the $15,000 a month retainer with the CTO and engineers included; then the model bill, $13.50 to $1,350 a month in our worked example for 1,000 active users. Published vendor ranges for an AI product MVP run $5,000 to $15,000 for an API wrapper and $25,000 to $60,000 for retrieval over your data (Space-O, October 2026), and $15,000 to $120,000 by tier (SFAI Labs, February 2026). Our MVP development cost guide has every figure with its source, and the custom software development cost guide covers builds beyond an MVP.
How much does the AI model cost per month?
Take 1,000 active users making 30 requests a month, each about 2,000 input and 500 output tokens: 60 million input and 15 million output tokens. At list prices on October 17, 2026, that's about $13.50 a month on Claude Haiku 5.5 or gpt-6-luna, $270 on Claude Sonnet 5.5 or gpt-6.1-sol, $540 on Claude Opus 5.5 and $1,350 on Claude Fable 5.1 or gpt-6-astra; the per-token prices and the links to both price lists are in the worked example above. Prompt caching and batch tiers lower those; a long document in every prompt raises them. The scope names the model and the estimate, and the monthly scorecard reports the real spend.
Can you fix my Lovable, Replit or Cursor app without rebuilding it?
Usually, yes, and keep-and-repair is the default. The $3,000 sprint audits the product, code and architecture, fixes the critical bugs and security holes, and gives you a written scorecard and roadmap. A rebuild is the cheaper route in two cases: the data model has to change under live customers, or the stack the tool chose blocks what the product must do next. If either applies, the scorecard says so with the reasoning, and you decide with the findings in hand. If the app was built for you by a developer who then left, our software project rescue page covers the takeover.
Do you use AI to build, and do I get the saving?
Yes to both. The tools write the first draft of code and tests; our engineers design the data model, review every change, write the tests the tools skip and run the security checks. The sprint is fixed and the retainers are flat, so when the tools make us faster you pay for fewer months, and the AI accounts and repos sit in your organisation.
Do I need my own data to build an AI product?
Only if the product has to answer from it. An API wrapper (a product layer over a hosted model with good prompts) needs no data of yours and sits at the bottom of every published range. Retrieval over your documents, tickets or records needs a data inventory first: what exists, where it lives, who may see it, and how clean it is. Space-O's cost guide puts it plainly: the jump between tiers "is driven by data and infrastructure, not interface work" (October 2026). The scope includes that inventory before a price is fixed.
How do we know the AI answers are right?
With an evaluation set: a list of real questions with known good answers, run after every change, with the pass rate in your scorecard. We scope evals, observability and guardrails in from the start (Bitsens budgets them at 15% to 30% of the AI portion of a build, May 2026) rather than after the first wrong answer reaches a customer. Gartner predicted that 30% of generative AI projects would be abandoned after proof of concept by the end of 2025 (cited by PM World Journal, January 2026); an evaluation set is the cheapest insurance against being one of them.
What about HIPAA, SOC 2 or customer data?
Say so on the call. SFAI Labs puts security and compliance requirements at 30% to 100% on top of an AI MVP's cost depending on the regime (February 2026), and we'd rather price that in the scope than discover it in month two. Your data stays in your own accounts, the model provider is chosen with its data terms in view, and the audit in the sprint checks who can see what before anyone else does.
Who owns the prompts, the AI accounts and the code?
You do, from the first commit, under a US-jurisdiction agreement with Deepsel Inc., a Delaware company. Repos, cloud accounts, model-provider accounts and documentation sit in your organisation from day one. Every plan is month-to-month with 30 days' notice, no termination fee, and a full refund if the first 30 days don't work out.
How long does an AI MVP take?
A typical startup MVP ships inside 1 to 2 months here, and an API-wrapper or retrieval MVP is that build plus one component. Published vendor claims: 3 to 6 weeks for a basic AI build and 8 to 14 weeks for a custom retrieval system (SFAI Labs, February 2026), and 8 to 24 weeks for "AI / Advanced" work (Minimum Code, September 2026). The sprint is one week, fixed. A custom or fine-tuned model is a different project, and the scope will say so before you commit.